Human Computer Interaction

Dr. Wolfgang Fuhl

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University of Tübingen
Dpt. of Computer Science
Human-Computer Interaction
Sand 14
72076 Tübingen
Germany

Telephone
+49 - (0) 70 71 - 29 - 70492
Telefax
+49 - (0) 70 71 - 29 - 50 62
E-Mail
wolfgang.fuhl@uni-tuebingen.de
Office
Sand 14, C206
Office hours
on appointment

Publications

Training Decision Trees as Replacement for Convolution Layers

by W. Fuhl, G. Kasneci, W. Rosenstiel, and E. Kasneci

In Conference on Artificial Intelligence, AAAI, 2020.

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Encodji: Encoding Gaze Data Into Emoji Space for an Amusing Scanpath Classification Approach ;)

by Wolfgang Fuhl, Efe Bozkir, Benedikt Hosp, Nora Castner, David Geisler, Thiago C., and Enkelejda Kasneci

In Eye Tracking Research and Applications, 2019.

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Ferns for area of interest free scanpath classification

by W. Fuhl, N. Castner, T. C. Kübler, A. Lotz, W. Rosenstiel, and E. Kasneci

In Proceedings of the 2019 ACM Symposium on Eye Tracking Research & Applications (ETRA) , 2019.

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Image-based extraction of eye features for robust eye tracking

by W. Fuhl

PhD thesis. University of Tübingen, 2019.

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500,000 images closer to eyelid and pupil segmentation

by W. Fuhl, W. Rosenstiel, and E. Kasneci

In Computer Analysis of Images and Patterns, CAIP, 2019.

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RemoteEye: An Open Source remote Eye Tracker

by B. Hosp, S. Evazi, M. Maurer, W. Fuhl, and E. Kasneci

In Behavior Research Methods, BRM, 2019.

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The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement

by W. Fuhl, D. Geisler, W. Rosenstiel, and E. Kasneci

In International Conference on Computer Vision Workshops, ICCVW, 2019.

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Learning to validate the quality of detected landmarks

by W. Fuhl and E. Kasneci

In International Conference on Machine Vision, ICMV, 2019.

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PuRe: Robust Pupil Detection for Real-Time Pervasive Eye Tracking

by T. Santini, W. Fuhl, and E. Kasneci

In Elsevier Computer Vision and Image Understanding To Appear, 2018.

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PuReST: Robust Pupil Tracking for Real-Time Pervasive Eye Tracking

by T. Santini, W. Fuhl, and E. Kasneci

In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA), 2018.

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CBF:Circular binary features for robust and real-time pupil center detection

by W. Fuhl, D. Geisler, T. Santini, T. Appel, W. Rosenstiel, and E. Kasneci

In ACM Symposium on Eye Tracking Research & Applications, 2018.

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Automatic generation of saliency-based areas of interest

by W. Fuhl, T. Kübler, T. Santini, and E. Kasneci

In Symposium on Vision, Modeling and Visualization (VMV), 2018.

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Region of interest generation algorithms for eye tracking data

by W. Fuhl, T. C. Kübler, H. Brinkmann, R. Rosenberg, W. Rosenstiel, and E. Kasneci

In Third Workshop on Eye Tracking and Visualization (ETVIS), in conjunction with ACM ETRA, 2018.

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MAM: Transfer learning for fully automatic video annotation and specialized detector creation

by W. Fuhl, N. Castner, L. Zhuang, M. Holzer, W. Rosenstiel, and E. Kasneci

In International Conference on Computer Vision Workshops, ICCVW, 2018.

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Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools

by W. Fuhl and E. Kasneci

In Poster at Egocentric Perception, Interaction and Computing, EPIC, 2018.

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BORE: Boosted-oriented edge optimization for robust, real time remote pupil center detection

by W. Fuhl, S. Eivazi, B. Hosp, A. Eivazi, W. Rosenstiel, and E. Kasneci

In Eye Tracking Research and Applications, ETRA, 2018.

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Rule based learning for eye movement type detection

by W. Fuhl, N. Castner, and E. Kasneci

In International Conference on Multimodal Interaction Workshops, ICMIW, 2018.

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Histogram of oriented velocities for eye movement detection

by W. Fuhl, N. Castner, and E. Kasneci

In International Conference on Multimodal Interaction Workshops, ICMIW, 2018.

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Saliency Sandbox: Bottom-Up Saliency Framework

by D. Geisler, W. Fuhl, T. Santini, and E. Kasneci

In 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.

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EyeRecToo: Open-Source Software for Real-Time Pervasive Head-Mounted Eye-Tracking

by T. Santini, W. Fuhl, D. Geisler, and E. Kasneci

In 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.

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EyeLad: Remote Eye Tracking Image Labeling Tool

by W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, and E. Kasneci

In 12th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 2017.

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Fast and Robust Eyelid Outline and Aperture Detection in Real-World Scenarios

by W. Fuhl, T. Santini, and E. Kasneci

In IEEE Winter Conference on Applications of Computer Vision (WACV 2017), 2017.

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Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection

by W. Fuhl, T. C. Kübler, D. Hospach, O. Bringmann, W. Rosenstiel, and E. Kasneci

In Journal of Eye Movement Research 10(3), 2017.

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CalibMe: Fast and Unsupervised Eye Tracker Calibration for Gaze-Based Pervasive Human-Computer Interaction

by T. Santini, W. Fuhl, and E. Kasneci

In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017.

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Towards Intelligent Surgical Microscopes: Surgeons Gaze and Instrument Tracking

by Shahram Eivazi, Wolfgang Fuhl, and Enkelejda Kasneci

In Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017. ACM, 2017.

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Towards automatic skill evaluation in microsurgery

by Shahram Eivazi, Michael Slupina, Wolfgang Fuhl, Hoorieh Afkari, Ahmad Hafez, and Enkelejda Kasneci

In Proceedings of the 22st International Conference on Intelligent User Interfaces, IUI 2017. ACM, 2017.

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PupilNet v2.0: Convolutional Neural Networks for Robust Pupil Detection

by W. Fuhl, T. Santini, G. Kasneci, and E. Kasneci

In CoRR, 2017.

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Fast camera focus estimation for gaze-based focus control

by W. Fuhl, T. Santini, and E. Kasneci

In CoRR, 2017.

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Optimal eye movement strategies: a comparison of neurosurgeons gaze patterns when using a surgical microscope

by S. Eivazi, A. Hafez, W. Fuhl, H. Afkari, E. Kasneci, M. Lehecka, and R. Bednarik

In Acta Neurochirurgica, 2017.

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EyeRec: An Open-source Data Acquisition Software for Head-mounted Eye-tracking

by T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 3: VISAPP: 386–391, 2016.

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ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments

by W. Fuhl, T. Santini, T. C. Kübler, and E. Kasneci

In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 123–130, 2016.

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Bayesian Identification of Fixations, Saccades, and Smooth Pursuits

by T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci

In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 163–170, 2016.

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Pupil detection for head-mounted eye tracking in the wild: An evaluation of the state of the art

by Wolfgang Fuhl, Marc Tonsen, Andreas Bulling, and Enkelejda Kasneci

In Machine Vision and Applications, pages 1-14, 2016.

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Eyes Wide Open? Eyelid Location and Eye Aperture Estimation for Pervasive Eye Tracking in Real-World Scenarios

by W. Fuhl, T. Santini, D. Geisler, T. C. Kübler, W. Rosenstiel, and E. Kasneci

In ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.

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Novel methods for analysis and visualization of saccade trajectories

by T. C. Kübler, W. Fuhl, R. Rosenberg, W. Rosenstiel, and E. Kasneci

3. ECCV Workshop VISART 2016, 2016.

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Non-Intrusive Practitioner Pupil Detection for Unmodified Microscope Oculars

by W. Fuhl, T. Santini, C. Reichert, D. Claus, A. Herkommer, H. Bahmani, K. Rifai, S. Wahl, and E. Kasneci

In Elsevier Computers in Biology and Medicine 79: 36-44, 2016.

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Evaluation of State-of-the-Art Pupil Detection Algorithms on Remote Eye Images

by W. Fuhl, D. Geisler, T. Santini, and E. Kasneci

In ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct publication – PETMEI 2016, 2016.

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Feature-based attentional influences on the accommodation response

by H. Bahmani, W. Fuhl, E. Gutierrez, G. Kasneci, E. Kasneci, and S. Wahl

In Vision Sciences Society Annual Meeting Abstract, 2016.

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PupilNet: Convolutional Neural Networks for Robust Pupil Detection

by W. Fuhl, T. Santini, G. Kasneci, and E. Kasneci

In CoRR, 2016.

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Analysis of eye movements with Eyetrace

by T. C. Kübler, K. Sippel, W. Fuhl, G. Schievelbein, J. Aufreiter, R. Rosenberg, W. Rosenstiel, and E. Kasneci

574: 458-471. Biomedical Engineering Systems and Technologies. Communications in Computer and Information Science (CCIS). Springer International Publishing, 2015.

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Eyetrace2014: Eyetracking Data Analysis Tool

by K. Sippel, T. C. Kübler, W. Fuhl, G. Schievelbein, R. Rosenberg, and W. Rosenstiel

In 8th International Conference on Health Informatics, Healthinf 2015, 2015.

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Exploiting the potential of eye movements analysis in the driving context

by E. Kasneci, T. C. Kübler, C. Braunagel, W. Fuhl, W. Stolzmann, and W. Rosenstiel

In 15. Internationales Stuttgarter Symposium Automobil- und Motorentechnik. Springer Fachmedien Wiesbaden, 2015.

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ExCuSe: Robust Pupil Detection in Real-World Scenarios

by W. Fuhl, T. C. Kübler, K. Sippel, W. Rosenstiel, and E. Kasneci

In 16th International Conference on Computer Analysis of Images and Patterns (CAIP 2015), 2015.

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Arbitrarily shaped areas of interest based on gaze density gradient

by W. Fuhl, T. C. Kübler, K. Sippel, W. Rosenstiel, and E. Kasneci

In European Conference on Eye Movements, ECEM 2015, 2015.

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Research

Eye labeling tool

Ground truth data is an important prerequisite for the development and evaluation of many algorithms in the area of computer vision, especially when these are based on convolutional neural networks or other machine learning approaches that unfold their power mostly by supervised learning. This learning relies on ground truth data, which is laborious, tedious, and error prone for humans to generate. In this paper, we contribute a labeling tool (EyeLad) specifically designed for remote eye-tracking data to enable researchers to leverage machine learning based approaches in this field, which is of great interest for the automotive, medical, and human-computer interaction applications. The tool is multi platform and supports a variety of state-of-theart detection and tracking algorithms, including eye detection, pupil detection, and eyelid coarse positioning.

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Eye Movements Identification

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Eyetrace

Eyetrace is a tool for analysis of eye-tracking data. It has the approach to bunch a variety of different evaluation methods for a large share of eye trackers supporting scientific work and medical diagnosis. To allow EyeTrace to be compatible to different eye trackers, an additional tool called Eyetrace Butler is used. The Eyetrace Butler performs a data preprocessing and conversion for analysis with Eyetrace. It provides plugins for different eye trackers and converts their data into a format that can be imported and used by Eyetrace.

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Intelligent Surgical Microscope

Head-mounted eye tracking offers remarkable opportunities for research and applications regarding pervasive health monitoring, mental state inference, and human computer interaction in dynamic scenarios. Although a plethora of software for the acquisition of eye-tracking data exists, they often exhibit critical issues when pervasive eye tracking is considered, e.g., closed source, costly eye tracker hardware dependencies, and requiring a human supervisor for calibration. In this paper, we introduce EyeRecToo, an open-source software for real-time pervasive head-mounted eye-tracking. Out of the box, EyeRecToo offers multiple real-time state-of-the-art pupil detection and gaze estimation methods, which can be easily replaced by user implemented algorithms if desired. A novel calibration method that allows users to calibrate the system without the assistance of a human supervisor is also integrated. Moreover, this software supports multiple head-mounted eye-tracking hardware, records eye and scene videos, and stores pupil and gaze information, which are also available as a real-time stream. Thus, EyeRecToo serves as a framework to quickly enable pervasive eye-tracking research and applications.

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Robust Pupil Detection and Gaze Estimation

The reliable estimation of the pupil position in eye images is perhaps the most important prerequisite in gaze-based HMI applications. While there are many approaches that enable accurate pupil tracking under laboratory conditions, tracking the pupil in real-world images is highly challenging due to changes in illumination, reflections on glasses or on the eyeball, off-axis camera position, contact lenses, and many more.

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Teaching

Course Term
Programmieren mobiler eingebetteter Systeme Winter 2016
Programmieren mobiler eingebetteter Systeme Winter 2013
Programmieren mobiler eingebetteter Systeme Winter 2011
Programmieren mobiler eingebetteter Systeme Winter 2015
Technische Anwendungen der Informatik: Hard- und Software aktueller Eye-Tracking-Systeme Summer 2016
Seminar: Advanced Topics in Perception Engineering Summer 2019

Finished Thesis Topics

Vein extraction and eye rotation determination

The first step is setting up an recording environment with fixed subject position. This environment is used for data acquisition with predefined head rotations of the subjects. Based on this data an algorithm has to be developed measuring the eyeball rotation of the subject. The resulting angle is then compared and validated based on the head rotation.

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EyeTrace CUDA extesion

EyeTrace is a software for gaze data visualization and analysis. Due to the increasing amount of data these visualizations need more computation time. In this thesis existing visualizations should be implemented using CUDA for GPU computations. Additionally this includes a data storage model making it possible to shift the data between the GPU and the host computer. Due to the fact that nowadays not all computers have a CUDA capable card the modul should also allow CPU computations. This should be determined automatically by the module.

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3D Eyeball generation based on vein motion

The first step is robust feature extraction. This can be done using SURF, SIFT, BRISK or MSER features if sufficient. Those features have to be mapped on features found in consecutive images. Based on the displacement a 3D model has to be computed. This model is used afterwards for gaze position estimation.

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